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Ontology Matching with Knowledge Rules

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9940))

Abstract

Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between these relationships to find more accurate alignments. We accomplish this by defining soft constraints that prefer alignments where corresponding concepts have the same local relationships encoded as knowledge rules. We use a probabilistic framework to integrate this new knowledge-based strategy with standard terminology-based and structure-based strategies. Furthermore, our method is particularly effective in identifying correspondences between complex concepts. Our method achieves better F-score than the state-of-the-art on three ontology matching domains.

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Notes

  1. 1.

    http://www.w3.org/TR/owl2-primer/.

  2. 2.

    Throughout the paper, we will use ontologies in the conference domain (cmt, confOf, conference, edas, ekaw) and the NBA domain (nba-os, yahoo) in our examples. The characteristics of these ontologies will be further described in Sect. 7.

  3. 3.

    http://code.google.com/p/thebeast/.

  4. 4.

    https://code.google.com/p/rockit/. We use RockIt for the census domain because TheBeast is not able to handle the large number of rules in that domain.

  5. 5.

    http://oaei.ontologymatching.org/2014/.

  6. 6.

    http://research.microsoft.com/en-us/um/people/dmax/WinMine/Tooldoc.htm.

  7. 7.

    https://archive.ics.uci.edu/ml/datasets.html.

  8. 8.

    We used MIRA implemented in TheBeast for weight learning.

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Acknowledgement

This research is being funded by NSF grant IIS-1118050. The authors would like to thank the generous comments from the anonymous reviewers. Their comments have greatly helped improve this research and prepare this paper.

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Oregon, Eugene, USA

    Shangpu Jiang, Daniel Lowd, Sabin Kafle & Dejing Dou

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  1. Shangpu Jiang
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Correspondence to Dejing Dou .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University , Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz , Linz, Austria

    Josef Küng

  3. FAW, University of Linz , Linz, Austria

    Roland Wagner

  4. HP Labs , Sunnyvale, California, USA

    Qimin Chen

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Jiang, S., Lowd, D., Kafle, S., Dou, D. (2016). Ontology Matching with Knowledge Rules. In: Hameurlain, A., Küng, J., Wagner, R., Chen, Q. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXVIII. Lecture Notes in Computer Science(), vol 9940. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53455-7_4

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  • DOI: https://doi.org/10.1007/978-3-662-53455-7_4

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